A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system

Energy ◽  
2021 ◽  
Vol 219 ◽  
pp. 119529
Author(s):  
Yuanjun Guo ◽  
Zhile Yang ◽  
Kailong Liu ◽  
Yanhui Zhang ◽  
Wei Feng
Keyword(s):  
Neural Network ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
State Of Charge ◽  
Network Approach ◽  
Neural Network Approach ◽  
Battery State Of Charge ◽  
State Of Charge Estimation

State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

2020 ◽  
Vol 56 (5) ◽  
pp. 5565-5574
Author(s):  
Dickshon N. T. How ◽  
Mahammad A. Hannan ◽  
Molla S. Hossain Lipu ◽  
Khairul S. M. Sahari ◽  
Pin Jern Ker ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Deep Neural Network ◽  
State Of Charge ◽  
Li Ion Battery ◽  
Network Approach ◽  
Neural Network Approach ◽  
Li Ion ◽  
State Of Charge Estimation

scholarly journals Simple and Effective Algorithm for Battery Assisted Quasi Z-Source Inverter for Standalone PV-Pumping Systems

2020 ◽  
Vol 9 (7) ◽  
pp. 1309-1314
Keyword(s):  
Energy Storage ◽  
Control Strategy ◽  
Storage System ◽  
Energy Storage System ◽  
State Of Charge ◽  
Duty Ratio ◽  
Effective Algorithm ◽  
Solar Panels ◽  
Switching Strategy ◽  
Battery State Of Charge

A QZSI with an energy storage system is developed for standalone applications. A controller based on the battery-assisted Quasi Z-Source Inverter model is designed to achieve both MPPT from the solar panels and to control the battery State of Charge (SOC). The control strategy will control both MPPT and Battery SOC through the shoot-through duty ratio (D) and Modulation index of the inverter (M). The simple boost modulation technique is adopted for the inverter switching strategy. The performance of the designed system is verified using simulation.

scholarly journals On the Investigation of Energy Efficient Torque Distribution Strategies through a Comprehensive Powertrain Model

2021 ◽  
Vol 13 (8) ◽  
pp. 4549
Author(s):  
Sara Salamone ◽  
Basilio Lenzo ◽  
Giovanni Lutzemberger ◽  
Francesco Bucchi ◽  
Luca Sani
Keyword(s):  
Energy Storage ◽  
Electric Vehicles ◽  
Energy Efficient ◽  
Storage System ◽  
Energy Storage System ◽  
Torque Distribution ◽  
Driving Cycles ◽  
Energy Models ◽  
Battery State Of Charge

In electric vehicles with multiple motors, the torque at each wheel can be controlled independently, offering significant opportunities for enhancing vehicle dynamics behaviour and system efficiency. This paper investigates energy efficient torque distribution strategies for improving the operational efficiency of electric vehicles with multiple motors. The proposed strategies are based on the minimisation of power losses, considering the powertrain efficiency characteristics, and are easily implementable in real-time. A longitudinal dynamics vehicle model is developed in Simulink/Simscape environment, including energy models for the electrical machines, the converter, and the energy storage system. The energy efficient torque distribution strategies are compared with simple distribution schemes under different standardised driving cycles. The effect of the different strategies on the powertrain elements, such as the electric machine and the energy storage system, are analysed. Simulation results show that the optimal torque distribution strategies provide a reduction in energy consumption of up to 5.5% for the case-study vehicle compared to simple distribution strategies, also benefiting the battery state of charge.

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